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fix(config): use new get_config across the app, use correct settings

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This commit is contained in:
psychedelicious 2024-03-11 23:01:48 +11:00
parent 7b1f9409bc
commit 897fe497dc
18 changed files with 36 additions and 32 deletions
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2
invokeai/app/api/dependencies.py
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@ -65,7 +65,7 @@ class ApiDependencies:
logger.info(f"InvokeAI version {__version__}")
logger.info(f"Root directory = {str(config.root_path)}")
output_folder = config.output_path
output_folder = config.outputs_path
if output_folder is None:
raise ValueError("Output folder is not set")

4
invokeai/app/invocations/__init__.py
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@ -3,9 +3,9 @@ import sys
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.services.config.config_default import get_config
custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.resolve())
custom_nodes_path = Path(get_config().custom_nodes_path.resolve())
custom_nodes_path.mkdir(parents=True, exist_ok=True)
custom_nodes_init_path = str(custom_nodes_path / "__init__.py")

4
invokeai/app/invocations/baseinvocation.py
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@ -33,7 +33,7 @@ from invokeai.app.invocations.fields import (
FieldKind,
Input,
)
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.services.config.config_default import get_config
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.metaenum import MetaEnum
from invokeai.app.util.misc import uuid_string
@ -191,7 +191,7 @@ class BaseInvocation(ABC, BaseModel):
@classmethod
def get_invocations(cls) -> Iterable[BaseInvocation]:
"""Gets all invocations, respecting the allowlist and denylist."""
app_config = InvokeAIAppConfig.get_config()
app_config = get_config()
allowed_invocations: set[BaseInvocation] = set()
for sc in cls._invocation_classes:
invocation_type = sc.get_type()

2
invokeai/app/invocations/latent.py
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@ -866,7 +866,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
vae.to(dtype=torch.float16)
latents = latents.half()
if self.tiled or context.config.get().tiled_decode:
if self.tiled or context.config.get().force_tiled_decode:
vae.enable_tiling()
else:
vae.disable_tiling()

8
invokeai/app/services/model_install/model_install_default.py
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@ -279,9 +279,9 @@ class ModelInstallService(ModelInstallServiceBase):
def sync_to_config(self) -> None:
"""Synchronize models on disk to those in the config record store database."""
self._scan_models_directory()
if autoimport := self._app_config.autoimport_dir:
if self._app_config.autoimport_path:
self._logger.info("Scanning autoimport directory for new models")
installed = self.scan_directory(self._app_config.root_path / autoimport)
installed = self.scan_directory(self._app_config.autoimport_path)
self._logger.info(f"{len(installed)} new models registered")
self._logger.info("Model installer (re)initialized")
@ -365,7 +365,7 @@ class ModelInstallService(ModelInstallServiceBase):
) -> Path:
"""Download the model file located at source to the models cache and return its Path."""
model_hash = sha256(str(source).encode("utf-8")).hexdigest()[0:32]
model_path = self._app_config.models_convert_cache_path / model_hash
model_path = self._app_config.convert_cache_path / model_hash
# We expect the cache directory to contain one and only one downloaded file.
# We don't know the file's name in advance, as it is set by the download
@ -591,7 +591,7 @@ class ModelInstallService(ModelInstallServiceBase):
# add 'main' specific fields
if isinstance(info, CheckpointConfigBase):
legacy_conf = (self.app_config.root_dir / self.app_config.legacy_conf_dir / info.config_path).resolve()
legacy_conf = (self.app_config.legacy_conf_path / info.config_path).resolve()
info.config_path = legacy_conf.as_posix()
self.record_store.add_model(info)
return info.key

6
invokeai/app/services/model_manager/model_manager_default.py
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@ -78,13 +78,13 @@ class ModelManagerService(ModelManagerServiceBase):
logger.setLevel(app_config.log_level.upper())
ram_cache = ModelCache(
max_cache_size=app_config.ram_cache_size,
max_vram_cache_size=app_config.vram_cache_size,
max_cache_size=app_config.ram,
max_vram_cache_size=app_config.vram,
logger=logger,
execution_device=execution_device,
)
convert_cache = ModelConvertCache(
cache_path=app_config.models_convert_cache_path, max_size=app_config.convert_cache_size
cache_path=app_config.convert_cache_path, max_size=app_config.convert_cache
)
loader = ModelLoadService(
app_config=app_config,

2
invokeai/app/services/session_queue/session_queue_sqlite.py
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@ -151,7 +151,7 @@ class SqliteSessionQueue(SessionQueueBase):
# TODO: how does this work in a multi-user scenario?
current_queue_size = self._get_current_queue_size(queue_id)
max_queue_size = self.__invoker.services.configuration.get_config().max_queue_size
max_queue_size = self.__invoker.services.configuration.max_queue_size
max_new_queue_items = max_queue_size - current_queue_size
priority = 0

2
invokeai/app/services/shared/invocation_context.py
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@ -423,7 +423,7 @@ class ConfigInterface(InvocationContextInterface):
The app's config.
"""
return self._services.configuration.get_config()
return self._services.configuration
class UtilInterface(InvocationContextInterface):

4
invokeai/backend/image_util/depth_anything/__init__.py
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@ -9,14 +9,14 @@ from einops import repeat
from PIL import Image
from torchvision.transforms import Compose
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.services.config.config_default import get_config
from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
from invokeai.backend.util.devices import choose_torch_device
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.backend.util.util import download_with_progress_bar
config = InvokeAIAppConfig.get_config()
config = get_config()
logger = InvokeAILogger.get_logger(config=config)
DEPTH_ANYTHING_MODELS = {

4
invokeai/backend/image_util/dw_openpose/wholebody.py
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@ -6,7 +6,7 @@ import pathlib
import numpy as np
import onnxruntime as ort
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.services.config.config_default import get_config
from invokeai.backend.util.devices import choose_torch_device
from invokeai.backend.util.util import download_with_progress_bar
@ -24,7 +24,7 @@ DWPOSE_MODELS = {
},
}
config = InvokeAIAppConfig.get_config()
config = get_config
class Wholebody:

6
invokeai/backend/image_util/lama.py
View File

@ -6,9 +6,11 @@ import torch
from PIL import Image
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config.config_default import get_config
from invokeai.backend.util.devices import choose_torch_device
config = get_config()
def norm_img(np_img):
if len(np_img.shape) == 2:
@ -29,7 +31,7 @@ def load_jit_model(url_or_path, device):
class LaMA:
def __call__(self, input_image: Image.Image, *args: Any, **kwds: Any) -> Any:
device = choose_torch_device()
model_location = get_invokeai_config().models_path / "core/misc/lama/lama.pt"
model_location = get_config().models_path / "core/misc/lama/lama.pt"
model = load_jit_model(model_location, device)
image = np.asarray(input_image.convert("RGB"))

4
invokeai/backend/image_util/patchmatch.py
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@ -8,9 +8,9 @@ be suppressed or deferred
import numpy as np
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.config.config_default import get_config
config = InvokeAIAppConfig.get_config()
config = get_config()
class PatchMatch:

4
invokeai/backend/image_util/txt2mask.py
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@ -33,11 +33,11 @@ from PIL import Image, ImageOps
from transformers import AutoProcessor, CLIPSegForImageSegmentation
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.config.config_default import get_config
CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
CLIPSEG_SIZE = 352
config = InvokeAIAppConfig.get_config()
config = get_config()
class SegmentedGrayscale(object):

2
invokeai/backend/model_manager/merge.py
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@ -118,7 +118,7 @@ class ModelMerger(object):
config = self._installer.app_config
store = self._installer.record_store
base_models: Set[BaseModelType] = set()
variant = None if self._installer.app_config.full_precision else "fp16"
variant = None if self._installer.app_config.precision == "float32" else "fp16"
assert (
len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference

4
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -21,7 +21,7 @@ from diffusers.utils.outputs import BaseOutput
from pydantic import Field
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.config.config_default import get_config
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
from invokeai.backend.ip_adapter.unet_patcher import UNetPatcher
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData
@ -251,7 +251,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
"""
if xformers is available, use it, otherwise use sliced attention.
"""
config = InvokeAIAppConfig.get_config()
config = get_config()
if config.attention_type == "xformers":
self.enable_xformers_memory_efficient_attention()
return

4
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
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@ -8,7 +8,7 @@ import torch
from diffusers import UNet2DConditionModel
from typing_extensions import TypeAlias
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.config.config_default import get_config
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
ConditioningData,
ExtraConditioningInfo,
@ -54,7 +54,7 @@ class InvokeAIDiffuserComponent:
:param model: the unet model to pass through to cross attention control
:param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
"""
config = InvokeAIAppConfig.get_config()
config = get_config()
self.conditioning = None
self.model = model
self.model_forward_callback = model_forward_callback

3
invokeai/backend/util/devices.py
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@ -7,11 +7,12 @@ import torch
from torch import autocast
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.config.config_default import get_config
CPU_DEVICE = torch.device("cpu")
CUDA_DEVICE = torch.device("cuda")
MPS_DEVICE = torch.device("mps")
config = InvokeAIAppConfig.get_config()
config = get_config()
def choose_torch_device() -> torch.device:

3
invokeai/backend/util/logging.py
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@ -181,6 +181,7 @@ from pathlib import Path
from typing import Any, Dict, Optional
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.config.config_default import get_config
try:
import syslog
@ -339,7 +340,7 @@ class InvokeAILogger(object): # noqa D102
@classmethod
def get_logger(
cls, name: str = "InvokeAI", config: InvokeAIAppConfig = InvokeAIAppConfig.get_config()
cls, name: str = "InvokeAI", config: InvokeAIAppConfig = get_config()
) -> logging.Logger: # noqa D102
if name in cls.loggers:
return cls.loggers[name]